20171205_C_standards and samples
Garrett
12/5/2017
title: “20171205 C standard GB” editor_options: chunk_output_type: console date: “2017-12-06” output: html_document: df_print: paged —
library(isoreader)
library(isoprocessorCUB)
library(readxl)
library(dplyr)
library(ggplot2)
library(plotly)
library(knitr)
library(stringr)
knitr::opts_chunk$set(
dev=c("png", "pdf"), dev.args=list(pdf = list(encoding="WinAnsi", useDingbats=FALSE)),
fig.keep="all", fig.path=file.path("plots", "20171203_C_standards_GB_"))Load data
# loading from stored files
collections <-
c(
"171201_alkanesTest_C_shortenedCapillary_ND.cf.rda",
"171204.cf.rda"
)
isofiles <- iso_read_continuous_flow(file.path("data", collections))## Info: preparing to read 2 data file(s)...## Info: reading file data/171201_alkanesTest_C_shortenedCapillary_ND.cf.rda with '.rda' reader## Info: loaded data for 16 data files from R Data Archive - checking loaded files for content consistency...## Info: reading file data/171204.cf.rda with '.rda' reader## Info: loaded data for 18 data files from R Data Archive - checking loaded files for content consistency...Files overview
isofiles %>%
iso_get_file_info(select = c(Analysis, starts_with("Id"), Preparation, `GC Method`, `Seed Oxidation`)) %>%
kable()## Info: aggregating file info from 34 data file(s)| file_id | Analysis | Identifier 1 | Identifier 2 | Preparation | GC Method | Seed Oxidation |
|---|---|---|---|---|---|---|
| BF2803____.dxf | BF2803 | NA | NA | NA | Disabled | 0 |
| BF2804__0_7V__.dxf | BF2804 | 0.7V | NA | NA | Disabled | 0 |
| BF2805__1_2V__.dxf | BF2805 | 1.2V | NA | NA | Disabled | 0 |
| BF2806__3V__.dxf | BF2806 | 3V | NA | NA | Disabled | 0 |
| BF2807__6V__.dxf | BF2807 | 6V | NA | NA | Disabled | 0 |
| BF2808____.dxf | BF2808 | NA | NA | NA | Disabled | 0 |
| BF2809____.dxf | BF2809 | NA | NA | NA | Disabled | 0 |
| BF2810____.dxf | BF2810 | NA | NA | NA | Disabled | 0 |
| BF2828_A5_2ul.dxf | BF2828 | A5 | 2ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 0 |
| BF2829_A5_0_5ul.dxf | BF2829 | A5 | 0.5ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 0 |
| BF2830_A5_0_2ul.dxf | BF2830 | A5 | 0.2ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2831_A5_0_5ul.dxf | BF2831 | A5 | 0.5ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2832_A5_1_5ul.dxf | BF2832 | A5 | 1.5ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2833_A5_3ul.dxf | BF2833 | A5 | 3ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2834_A5_5ul.dxf | BF2834 | A5 | 5ul | 14ng per uL | OGL Methods71112 GC DB1 60m CH alkanes PTV high volume.tr2.meth | 1 |
| BF2835_A5_8ul.dxf | BF2835 | A5 | 8ul | 14ng per uL | OGL Methods71112 GC DB1 60m CH alkanes PTV high volume.tr2.meth | 1 |
| BF2843_CO2 zero_6V.dxf | BF2843 | CO2 zero | 6V | NA | Disabled | 0 |
| BF2844_CO2 zero_3V.dxf | BF2844 | CO2 zero | 3V | NA | Disabled | 0 |
| BF2845_CO2 zero_1_2V.dxf | BF2845 | CO2 zero | 1.2V | NA | Disabled | 0 |
| BF2846_CO2 zero_0_7V.dxf | BF2846 | CO2 zero | 0.7V | NA | Disabled | 0 |
| BF2847__Linearity CO2__.dxf | BF2847 | Linearity CO2 | NA | NA | Disabled | 0 |
| BF2848__Linearity CO2__.dxf | BF2848 | Linearity CO2 | NA | NA | Disabled | 0 |
| BF2849__Linearity CO2__.dxf | BF2849 | Linearity CO2 | NA | NA | Disabled | 0 |
| BF2850_A5_0_2ul.dxf | BF2850 | A5 | 0.2ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2851_A5_0_5ul.dxf | BF2851 | A5 | 0.5ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2852_A5_1_5ul.dxf | BF2852 | A5 | 1.5ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2853_A5_3ul.dxf | BF2853 | A5 | 3ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2854_A5_0_2ul.dxf | BF2854 | A5 | 0.2ul | 140ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2855_A5_0_5ul.dxf | BF2855 | A5 | 0.5ul | 140ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2856_A5_0_2ul.dxf | BF2856 | A5 | 0.2ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2857_A5_0_5ul.dxf | BF2857 | A5 | 0.5ul | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2885_A5_1_5uL.dxf | BF2885 | A5 | 1.5uL | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 0 |
| BF2886_A5_3uL.dxf | BF2886 | A5 | 3uL | 14ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
| BF2887_A5_0_5uL.dxf | BF2887 | A5 | 0.5uL | 140ng per uL | OGL Methods71024 GC DB1 60m CH alkanes PTV.tr2.meth | 1 |
Chromatogram overview
isofiles %>%
# select only the standards
iso_filter_files(`Identifier 1` == "A5") %>%
iso_plot_continuous_flow_data(data = "44", time_interval = c(1000, 3000), panel_by = "file", zoom = 1) +
theme(legend.position = "none") +
facet_wrap(~file_id)## Info: applying file filter, keeping 19 of 34 files
Pull out relevant information
# data table
data_raw <- isofiles %>%
# aggregate all relevant fields
iso_get_vendor_data_table(
select = c(Nr., Start, Rt, End, ends_with("44"), starts_with("R "), starts_with("d ")),
include_file_info = c(file_datetime, file_path, Analysis, `GC Method`, starts_with("Id"), Preparation, `Seed Oxidation`)
) %>%
# add folder and analysis number
mutate(
folder = basename(dirname(file_path)),
analysis = parse_number(Analysis)
)## Info: aggregating vendor data table without units from 34 data file(s), including file info 'c(file_datetime, file_path, Analysis, `GC Method`, starts_with("Id"),
## Preparation, `Seed Oxidation`)'Add information
Retrieve metadata
metadata_samples <- read_excel(file.path("metadata", "171201_GB_metadata.xlsx"), sheet = "files")
metadata_peak_maps <- read_excel(file.path("metadata","171201_GB_metadata.xlsx"), sheet = "maps")
kable(metadata_samples)| Identifier 1 | Analysis | map_id | type | process | notes |
|---|---|---|---|---|---|
| A5 | NA | A6.d1 | standard | yes | NA |
Map peaks
data_w_peaks <-
data_raw %>%
iso_set_default_process_parameters(
map_id = map_id,
rt = Rt,
rt_start = Start,
rt_end = End,
file_id = file_id,
print_func = kable
) %>%
iso_show_default_processor_parameters() %>%
# add in metadata
iso_add_metadata(metadata_samples, match_by = c(`Identifier 1`, Analysis)) %>%
iso_print_data_table (select = c(file_id, Analysis, `Identifier 1`), filter = !has_metadata,
title = "Info: records with missing metadata:") %>%
# only continue with records that have metadata and those that are flagged for processing
filter(has_metadata, process == "yes") %>%
# map peaks
iso_map_peaks(metadata_peak_maps) %>%
iso_print_data_table(select = c(file_id, compound, Start, Rt, End), filter = !is_identified | is_missing,
title = "Info: missing and unidentified peaks") %>%
# filter out the problematic peaks
filter(is_identified, !is_missing, !is_ambiguous)## Info: isoprocessor package current default parameters| parameter | value |
|---|---|
| quiet | FALSE |
| print_func | kable |
| map_id | map_id |
| rt | Rt |
| rt_start | Start |
| rt_end | End |
| file_id | file_id |
## Info: metadata added to 471 data rows, 132 left without metadata:
## - 1 metadata entries were mapped to 471 data entires via column 'Identifier 1'## Info: records with missing metadata:| file_id | Analysis | Identifier 1 |
|---|---|---|
| BF2803____.dxf | BF2803 | NA |
| BF2804__0_7V__.dxf | BF2804 | 0.7V |
| BF2805__1_2V__.dxf | BF2805 | 1.2V |
| BF2806__3V__.dxf | BF2806 | 3V |
| BF2807__6V__.dxf | BF2807 | 6V |
| BF2808____.dxf | BF2808 | NA |
| BF2809____.dxf | BF2809 | NA |
| BF2810____.dxf | BF2810 | NA |
| BF2843_CO2 zero_6V.dxf | BF2843 | CO2 zero |
| BF2844_CO2 zero_3V.dxf | BF2844 | CO2 zero |
| BF2845_CO2 zero_1_2V.dxf | BF2845 | CO2 zero |
| BF2846_CO2 zero_0_7V.dxf | BF2846 | CO2 zero |
| BF2847__Linearity CO2__.dxf | BF2847 | Linearity CO2 |
| BF2848__Linearity CO2__.dxf | BF2848 | Linearity CO2 |
| BF2849__Linearity CO2__.dxf | BF2849 | Linearity CO2 |
## Info: 448 peaks in 19 files were successfully assigned, 23 could not be assigned, and 44 were missing## Info: missing and unidentified peaks| file_id | compound | Start | Rt | End |
|---|---|---|---|---|
| BF2829_A5_0_5ul.dxf | NA | 2757.755 | 2762.980 | 2772.594 |
| BF2829_A5_0_5ul.dxf | C-30 | NA | 2773.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 1398.419 | 1402.390 | 1408.660 |
| BF2830_A5_0_2ul.dxf | C-18 | NA | 1409.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 1528.626 | 1532.806 | 1539.285 |
| BF2830_A5_0_2ul.dxf | C-19 | NA | 1540.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 1658.415 | 1662.595 | 1669.283 |
| BF2830_A5_0_2ul.dxf | C-20 | NA | 1670.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 1785.487 | 1789.667 | 1796.355 |
| BF2830_A5_0_2ul.dxf | C-21 | NA | 1797.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 1908.797 | 1912.977 | 1919.665 |
| BF2830_A5_0_2ul.dxf | C-22 | NA | 1921.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2027.927 | 2032.316 | 2039.004 |
| BF2830_A5_0_2ul.dxf | C-23 | NA | 2040.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2143.086 | 2147.475 | 2154.163 |
| BF2830_A5_0_2ul.dxf | C-24 | NA | 2156.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2254.274 | 2258.872 | 2265.769 |
| BF2830_A5_0_2ul.dxf | C-25 | NA | 2267.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2361.909 | 2366.298 | 2373.404 |
| BF2830_A5_0_2ul.dxf | C-26 | NA | 2375.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2465.991 | 2470.380 | 2477.486 |
| BF2830_A5_0_2ul.dxf | C-27 | NA | 2479.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2566.729 | 2571.118 | 2578.224 |
| BF2830_A5_0_2ul.dxf | C-28 | NA | 2580.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2664.123 | 2668.512 | 2675.827 |
| BF2830_A5_0_2ul.dxf | C-29 | NA | 2678.000 | NA |
| BF2830_A5_0_2ul.dxf | NA | 2758.382 | 2763.189 | 2770.295 |
| BF2830_A5_0_2ul.dxf | C-30 | NA | 2773.000 | NA |
| BF2852_A5_1_5ul.dxf | NA | 1283.678 | 1284.932 | 1297.681 |
| BF2853_A5_3ul.dxf | NA | 1238.325 | 1241.460 | 1247.103 |
| BF2853_A5_3ul.dxf | NA | 1282.424 | 1283.678 | 1296.218 |
| BF2853_A5_3ul.dxf | NA | 1296.218 | 1301.234 | 1311.266 |
| BF2853_A5_3ul.dxf | NA | 1355.783 | 1359.127 | 1363.516 |
| BF2853_A5_3ul.dxf | NA | 1867.833 | 1871.386 | 1875.984 |
| BF2853_A5_3ul.dxf | NA | 2017.895 | 2028.972 | 2037.750 |
| BF2853_A5_3ul.dxf | NA | 2156.880 | 2167.121 | 2213.937 |
| BF2853_A5_3ul.dxf | NA | 2288.341 | 2316.556 | 2346.443 |
| BF2856_A5_0_2ul.dxf | C-16 | NA | 1156.000 | NA |
| BF2856_A5_0_2ul.dxf | C-17 | NA | 1280.000 | NA |
| BF2856_A5_0_2ul.dxf | C-18 | NA | 1409.000 | NA |
| BF2856_A5_0_2ul.dxf | C-19 | NA | 1540.000 | NA |
| BF2856_A5_0_2ul.dxf | C-20 | NA | 1670.000 | NA |
| BF2856_A5_0_2ul.dxf | C-21 | NA | 1797.000 | NA |
| BF2856_A5_0_2ul.dxf | C-22 | NA | 1921.000 | NA |
| BF2856_A5_0_2ul.dxf | C-23 | NA | 2040.000 | NA |
| BF2856_A5_0_2ul.dxf | C-24 | NA | 2156.000 | NA |
| BF2856_A5_0_2ul.dxf | C-25 | NA | 2267.000 | NA |
| BF2856_A5_0_2ul.dxf | C-26 | NA | 2375.000 | NA |
| BF2856_A5_0_2ul.dxf | C-27 | NA | 2479.000 | NA |
| BF2856_A5_0_2ul.dxf | C-28 | NA | 2580.000 | NA |
| BF2856_A5_0_2ul.dxf | C-29 | NA | 2678.000 | NA |
| BF2856_A5_0_2ul.dxf | C-30 | NA | 2773.000 | NA |
| BF2857_A5_0_5ul.dxf | C-16 | NA | 1156.000 | NA |
| BF2857_A5_0_5ul.dxf | C-17 | NA | 1280.000 | NA |
| BF2857_A5_0_5ul.dxf | C-18 | NA | 1409.000 | NA |
| BF2857_A5_0_5ul.dxf | C-19 | NA | 1540.000 | NA |
| BF2857_A5_0_5ul.dxf | C-20 | NA | 1670.000 | NA |
| BF2857_A5_0_5ul.dxf | C-21 | NA | 1797.000 | NA |
| BF2857_A5_0_5ul.dxf | C-22 | NA | 1921.000 | NA |
| BF2857_A5_0_5ul.dxf | C-23 | NA | 2040.000 | NA |
| BF2857_A5_0_5ul.dxf | C-24 | NA | 2156.000 | NA |
| BF2857_A5_0_5ul.dxf | C-25 | NA | 2267.000 | NA |
| BF2857_A5_0_5ul.dxf | C-26 | NA | 2375.000 | NA |
| BF2857_A5_0_5ul.dxf | C-27 | NA | 2479.000 | NA |
| BF2857_A5_0_5ul.dxf | C-28 | NA | 2580.000 | NA |
| BF2857_A5_0_5ul.dxf | C-29 | NA | 2678.000 | NA |
| BF2857_A5_0_5ul.dxf | C-30 | NA | 2773.000 | NA |
Reference peaks
data_w_peaks %>%
iso_set_default_process_parameters(
group_id = Analysis,
ratio = `R 13C/12C`
) %>%
iso_visualize_ref_peaks(is_ref_condition = is_ref_peak == "yes") 
data_w_peaks %>%
iso_visualize_ref_peaks(is_ref_condition = is_ref_peak == "yes", within_group = TRUE) 
Process data
Focus on the analytes and calculate a few summary parameters we want to use later.
data_w_analyte_peaks <-
data_w_peaks %>%
# this is important so that the reference peaks are not caught up in the next set of calculations
filter(is_ref_peak == "no") %>%
# for each analysis calculate averages across analysis
group_by(Analysis) %>%
mutate(
ampl_sample_mean.mV = mean(`Ampl 44`), ampl_sample_sd.mV = sd(`Ampl 44`),
area_sample_mean.Vs = mean(`Intensity 44`), area_sample_sd.Vs = sd(`Intensity 44`)
)Standards
standards <- read_excel(file.path("metadata", "GC-IRMS standards.xlsx"))
kable(standards)| compound | true_d2H | true_d13C |
|---|---|---|
| C-16 | -9.1 | -26.15 |
| C-17 | -117.5 | -31.88 |
| C-18 | -52.0 | -32.70 |
| C-19 | -56.3 | -31.99 |
| C-20 | -89.7 | -33.97 |
| C-21 | -177.8 | -28.83 |
| C-22 | -81.3 | -33.77 |
| C-23 | -67.2 | -33.37 |
| C-24 | -29.7 | -32.13 |
| C-25 | -265.6 | -28.46 |
| C-26 | -45.9 | -32.94 |
| C-27 | -172.8 | -30.49 |
| C-28 | -36.8 | -33.20 |
| C-29 | -177.8 | -29.10 |
| C-30 | -213.6 | -29.84 |
data_w_stds <-
data_w_analyte_peaks %>%
iso_add_standards(standards) ## Info: added 15 standard entries to 241 out of 241 rowsCalibration
data_w_calibs <- data_w_stds %>%
# prepare for calibration by defining the grouping column(s) and setting default parameters
iso_prepare_for_calibration(group_by = c(Analysis)) %>%
iso_set_default_process_parameters(delta_residual = resid_d13C) %>%
# run calibration
iso_calibrate_delta(model = lm(`d 13C/12C` ~ true_d13C)) %>%
# pull out some columns we want generally available
iso_unnest_calib_data(select = c(starts_with("Id"), ampl_sample_mean.mV)) ## Info: preparing data for calibration by grouping based on 'Analysis' and nesting the grouped datasets into 'calibration_data'## Info: calculating delta calibration fits based on 1 model ('lm(`d 13C/12C` ~ true_d13C)') for 17 data group(s) in 'calibration_data' with filter 'is_standard'; storing residuals in 'resid_d13C.'data_w_calibs %>%
iso_unnest_delta_calib_summary(keep_other_list_data = FALSE) %>%
kable(digits =3)| Analysis | Identifier 1 | Identifier 2 | ampl_sample_mean.mV | r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BF2828 | A5 | 2ul | 2941.484 | 0.999 | 0.999 | 0.074 | 13953.274 | 0 | 2 | 18.847 | -31.693 | -29.569 | 0.071 | 13 |
| BF2829 | A5 | 0.5ul | 626.365 | 0.997 | 0.996 | 0.143 | 3697.360 | 0 | 2 | 8.455 | -10.910 | -8.993 | 0.245 | 12 |
| BF2830 | A5 | 0.2ul | 206.206 | 1.000 | NaN | NaN | NaN | NaN | 2 | Inf | -Inf | -Inf | 0.000 | 0 |
| BF2831 | A5 | 0.5ul | 683.263 | 0.981 | 0.980 | 0.336 | 679.300 | 0 | 2 | -3.833 | 13.665 | 15.789 | 1.464 | 13 |
| BF2832 | A5 | 1.5ul | 2397.444 | 0.997 | 0.997 | 0.122 | 5062.710 | 0 | 2 | 11.321 | -16.641 | -14.517 | 0.194 | 13 |
| BF2833 | A5 | 3ul | 3409.901 | 0.999 | 0.999 | 0.083 | 10814.818 | 0 | 2 | 17.082 | -28.165 | -26.041 | 0.090 | 13 |
| BF2834 | A5 | 5ul | 6366.513 | 0.952 | 0.949 | 0.518 | 260.647 | 0 | 2 | -10.343 | 26.685 | 28.809 | 3.487 | 13 |
| BF2835 | A5 | 8ul | 9463.344 | 0.796 | 0.780 | 1.201 | 50.709 | 0 | 2 | -22.959 | 51.918 | 54.042 | 18.753 | 13 |
| BF2850 | A5 | 0.2ul | 469.382 | 0.992 | 0.991 | 0.216 | 1624.493 | 0 | 2 | 2.743 | 0.514 | 2.638 | 0.609 | 13 |
| BF2851 | A5 | 0.5ul | 1859.622 | 0.996 | 0.996 | 0.147 | 3519.402 | 0 | 2 | 8.554 | -11.109 | -8.984 | 0.281 | 13 |
| BF2852 | A5 | 1.5ul | 6565.095 | 0.976 | 0.974 | 0.367 | 535.619 | 0 | 2 | -5.165 | 16.331 | 18.455 | 1.749 | 13 |
| BF2853 | A5 | 3ul | 10855.855 | 0.749 | 0.730 | 1.574 | 38.814 | 0 | 2 | -27.011 | 60.021 | 62.146 | 32.188 | 13 |
| BF2854 | A5 | 0.2ul | 1460.165 | 0.995 | 0.994 | 0.175 | 2390.234 | 0 | 2 | 5.895 | -5.789 | -3.665 | 0.400 | 13 |
| BF2855 | A5 | 0.5ul | 4192.515 | 0.990 | 0.990 | 0.234 | 1342.784 | 0 | 2 | 1.546 | 2.907 | 5.032 | 0.715 | 13 |
| BF2885 | A5 | 1.5uL | 1516.386 | 0.860 | 0.849 | 0.951 | 79.814 | 0 | 2 | -19.454 | 44.908 | 47.032 | 11.752 | 13 |
| BF2886 | A5 | 3uL | 2501.210 | 0.973 | 0.971 | 0.406 | 465.060 | 0 | 2 | -6.706 | 19.413 | 21.537 | 2.148 | 13 |
| BF2887 | A5 | 0.5uL | 3982.941 | 0.838 | 0.826 | 1.009 | 67.239 | 0 | 2 | -20.345 | 46.691 | 48.815 | 13.235 | 13 |
data_w_calibs %>%
# pull out seed oxidation in addition
iso_unnest_calib_data(select = `Seed Oxidation`) %>%
iso_unnest_delta_calib_coefs(select = c(-statistic), keep_other_list_data = FALSE) %>%
# arrange by term and Analysis to get a quick idea of what the numbers across analyses
arrange(term, Analysis) %>%
kable(digits = 2)| Analysis | Identifier 1 | Identifier 2 | ampl_sample_mean.mV | Seed Oxidation | term | estimate | std.error | p.value | signif |
|---|---|---|---|---|---|---|---|---|---|
| BF2828 | A5 | 2ul | 2941.48 | 0 | (Intercept) | 10.95 | 0.27 | 0.00 | *** |
| BF2829 | A5 | 0.5ul | 626.37 | 0 | (Intercept) | 11.30 | 0.52 | 0.00 | *** |
| BF2830 | A5 | 0.2ul | 206.21 | 1 | (Intercept) | 13.79 | NaN | NaN | NA |
| BF2831 | A5 | 0.5ul | 683.26 | 1 | (Intercept) | 10.75 | 1.21 | 0.00 | *** |
| BF2832 | A5 | 1.5ul | 2397.44 | 1 | (Intercept) | 10.80 | 0.44 | 0.00 | *** |
| BF2833 | A5 | 3ul | 3409.90 | 1 | (Intercept) | 10.69 | 0.30 | 0.00 | *** |
| BF2834 | A5 | 5ul | 6366.51 | 1 | (Intercept) | 9.84 | 1.87 | 0.00 | *** |
| BF2835 | A5 | 8ul | 9463.34 | 1 | (Intercept) | 10.04 | 4.33 | 0.04 | * |
| BF2850 | A5 | 0.2ul | 469.38 | 1 | (Intercept) | 10.61 | 0.78 | 0.00 | *** |
| BF2851 | A5 | 0.5ul | 1859.62 | 1 | (Intercept) | 10.73 | 0.53 | 0.00 | *** |
| BF2852 | A5 | 1.5ul | 6565.10 | 1 | (Intercept) | 10.13 | 1.32 | 0.00 | *** |
| BF2853 | A5 | 3ul | 10855.86 | 1 | (Intercept) | 13.85 | 5.67 | 0.03 | * |
| BF2854 | A5 | 0.2ul | 1460.17 | 1 | (Intercept) | 10.27 | 0.63 | 0.00 | *** |
| BF2855 | A5 | 0.5ul | 4192.52 | 1 | (Intercept) | 10.50 | 0.85 | 0.00 | *** |
| BF2885 | A5 | 1.5uL | 1516.39 | 0 | (Intercept) | 9.42 | 3.43 | 0.02 | * |
| BF2886 | A5 | 3uL | 2501.21 | 1 | (Intercept) | 10.72 | 1.47 | 0.00 | *** |
| BF2887 | A5 | 0.5uL | 3982.94 | 1 | (Intercept) | 8.57 | 3.64 | 0.03 | * |
| BF2828 | A5 | 2ul | 2941.48 | 0 | true_d13C | 1.01 | 0.01 | 0.00 | *** |
| BF2829 | A5 | 0.5ul | 626.37 | 0 | true_d13C | 1.01 | 0.02 | 0.00 | *** |
| BF2830 | A5 | 0.2ul | 206.21 | 1 | true_d13C | 1.10 | NaN | NaN | NA |
| BF2831 | A5 | 0.5ul | 683.26 | 1 | true_d13C | 1.01 | 0.04 | 0.00 | *** |
| BF2832 | A5 | 1.5ul | 2397.44 | 1 | true_d13C | 1.00 | 0.01 | 0.00 | *** |
| BF2833 | A5 | 3ul | 3409.90 | 1 | true_d13C | 1.00 | 0.01 | 0.00 | *** |
| BF2834 | A5 | 5ul | 6366.51 | 1 | true_d13C | 0.96 | 0.06 | 0.00 | *** |
| BF2835 | A5 | 8ul | 9463.34 | 1 | true_d13C | 0.98 | 0.14 | 0.00 | *** |
| BF2850 | A5 | 0.2ul | 469.38 | 1 | true_d13C | 1.00 | 0.02 | 0.00 | *** |
| BF2851 | A5 | 0.5ul | 1859.62 | 1 | true_d13C | 1.00 | 0.02 | 0.00 | *** |
| BF2852 | A5 | 1.5ul | 6565.10 | 1 | true_d13C | 0.98 | 0.04 | 0.00 | *** |
| BF2853 | A5 | 3ul | 10855.86 | 1 | true_d13C | 1.13 | 0.18 | 0.00 | *** |
| BF2854 | A5 | 0.2ul | 1460.17 | 1 | true_d13C | 0.99 | 0.02 | 0.00 | *** |
| BF2855 | A5 | 0.5ul | 4192.52 | 1 | true_d13C | 0.99 | 0.03 | 0.00 | *** |
| BF2885 | A5 | 1.5uL | 1516.39 | 0 | true_d13C | 0.98 | 0.11 | 0.00 | *** |
| BF2886 | A5 | 3uL | 2501.21 | 1 | true_d13C | 1.01 | 0.05 | 0.00 | *** |
| BF2887 | A5 | 0.5uL | 3982.94 | 1 | true_d13C | 0.95 | 0.12 | 0.00 | *** |
Standards
Parameters
data_params <- data_w_calibs %>%
# pull out remaining columns we want available (some might already be pulled out but that's okay)
iso_unnest_calib_data(select = c(file_datetime, ampl_sample_mean.mV, Preparation, is_standard))
# visualize the delta calibration fits
data_params %>%
iso_visualize_delta_calib_fits(x = Analysis, color = `Identifier 2`, shape = Preparation, size = ampl_sample_mean.mV,
include_from_summary = c(adj.r.squared, deviance)) + labs(title = "parameters vs. analysis")
data_params %>%
iso_visualize_delta_calib_fits(x = ampl_sample_mean.mV, color = `Identifier 2`, shape = Preparation, size = ampl_sample_mean.mV,
include_from_summary = c(adj.r.squared, deviance)) + labs(title = "parameters vs. amplitude") 
data_params %>%
iso_visualize_delta_calib_fits(x = file_datetime, color = `Identifier 2`, shape = Preparation, size = ampl_sample_mean.mV,
include_from_summary = c(adj.r.squared, deviance)) + labs(title = "parameters vs. time")
# turn the last plot into an interactive one
ggplotly(ggplot2::last_plot() + theme(legend.position = "none"))Compounds
data_w_calibs %>%
# pull out relevant data
iso_unnest_calib_data(select = everything()) %>%
filter(is_standard) %>%
# calculate deviation from means
group_by(Analysis) %>%
mutate(
`Var: residual d13C [permil]` = resid_d13C,
`Var: area diff from mean [%]` = (`Intensity 44`/mean(`Intensity 44`) - 1) * 100,
`Var: amplitude diff from mean [%]` = (`Ampl 44`/mean(`Ampl 44`) - 1) * 100
) %>%
# visualize
iso_visualize_data(x = compound, y = starts_with("Var"), group = Analysis, color = `Identifier 2`)
ggplotly(ggplot2::last_plot())Data
FIXME: continue here with inverting calibration